Hybrid model to simulate host cell biomechanics and infection spread during intracellular infection of epithelial monolayers

Aparicio-Yuste, Raul ; Hundsdorfer, Lara ; Bastounis, Effie E. ; Gomez-Benito, Maria Jose

Resumen: Mechanical signals are crucial in regulating the response of cells in a monolayer to both physiological and pathological stressors, including intracellular bacterial infections. In particular, during intracellular infection of epithelial cells in monolayer with the food-borne bacterial pathogen Listeria monocytogenes, cellular biomechanics dictates the degree of bacterial dissemination across the monolayer. This occurs through a process whereby surrounder uninfected cells mechanically compete and eventually extrude infected cells. However, the plethora of physical mechanisms involved and their temporal dynamics are still not fully uncovered, which could inform whether they benefit or harm the host. To further investigate these mechanisms, we propose a two-dimensional hybrid computational model that combines an agent-based model with a finite element method to simulate the kinematics and dynamics of epithelial cell monolayers in the absence or presence of infection. The model accurately replicated the impact of cell density on the mechanical behaviour of uninfected monolayers, showing that increased cell density reduces cell motility and coordination of motion, cell fluidity and monolayer stresses. Moreover, when simulating the intercellular spread of infection, the model successfully reproduced the mechanical competition between uninfected and infected cells. Infected cells showed a reduction in cell area, while the surrounder cells migrated towards the infection site, exerting increased monolayer stresses, consistent with our in vitro observations. This model offers a powerful tool for studying epithelial monolayers in health and disease, by providing in silico predictions of cell shapes, kinematics and dynamics that can then be tested experimentally.

Idioma: Inglés

DOI: 10.1016/j.compbiomed.2024.109506

Año: 2024

Publicado en: Computers in biology and medicine 185 (2024), 109506 [13 pp.]
ISSN: 0010-4825

Factor impacto JCR: 6.3 (2024)

Categ. JCR: BIOLOGY rank: 7 / 107 = 0.065 (2024) - Q1 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 4 / 67 = 0.06 (2024) - Q1 - T1
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 22 / 124 = 0.177 (2024) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 26 / 175 = 0.149 (2024) - Q1 - T1

Factor impacto SCIMAGO: 1.447 - Health Informatics (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2021-124271OB-I00

Tipo y forma: Article (Published version)

Dataset asociado: The code implemented for the hybrid model used in this study ( github.com/raparicio21/monolayer_hybrid_model.git)